No abstract

This paper presents a generic approach for automatic singing assessment for basic singing levels. The system provides the user with a set of intonation, rhythm and overall ratings obtained by measuring the similarity of the sung melody and a target performance. Two different similarity approaches are discussed: f 0 curve alignment through Dynamic Time Warping (DTW), and singing transcription plus note-level similarity. From these two approaches, we extract different intonation and rhythm similarity measures which are combined through quadratic polynomial regression analysis in order to fit the judgement of 4 trained musicians on 27 performances. The results show that the proposed system is suitable for automatic singing voice rating and that DTW based measures are specially simple and effective for intonation and rhythm assessment.

In this paper, we propose a method to synthesize the natural variations of spectral envelope as intensity varies in singing voice. To this end, we propose a parametric model of spectral envelope based on novel 4-pole resonators as formant filters. This model has been used to analyse 60 vowels sung at different intensities in order to define a set of functions describing the global variations of parameters along intensity. These functions have been used to modify the intensity of 16 recorded vowels and 8 synthetic vowels generated with Vocaloid. The realism of the transformations performed with our approach has been evaluated by four amateur musicians in comparison to Melodyne for real sounds and to Vocaloid for synthetic sounds. The proposed approach has been proved to achieve more realistic sounds than Melodyne and Vocaloid, especially for loud-to-weak transformations.

Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high interannotator agreement percentages, which allows us to validate the annotation methodology and ensure the annotations reliability. In this work, we first review the current publicly available music detection datasets and state OpenBMAT's contributions. After that, we detail its building process: the selection of the audio and the annotation methodology. Then, we analyze the produced annotations and validate their reliability. We continue with an experiment to highlight the value of these annotations and investigate the most challenging content in OpenBMAT. Finally, we describe the details about the format in which the dataset is presented and the platform where we have made it available. We believe OpenBMAT will contribute to major advancements of the research on music detection in real-life scenarios.

Physical activity encompasses a series of overall benefits on cardiovascular health and metabolic disorders. Research has recently focused on the hepatobiliary tract, as an additional target of the health-related outcomes of different types of physical exercise. Here, we focus on the global features of physical activity with respect to exercise modality and intensity, and on studies linking physical activity to lipid metabolism, gallbladder diseases (gallstones, symptoms, complications and health-related quality of life), gallbladder motor-function, enterohepatic circulation of bile acids, and systemic metabolic inflammation. Additional studies need to unravel the pathophysiological mechanisms involved in both beneficial and harmful effects of physical activity in populations with different metabolic conditions.